Visualization
Jiaxuan Lyu
2021/2/21
Trip Data Analysis
Set up
#packages
library(tidyverse)
library(sf)
library(lubridate)
library(mapview)
library(viridis)
library(ggplot2)
library(kableExtra)##Functions
#Plot functions
plotTheme <- theme(
plot.title =element_text(size=12),
plot.subtitle = element_text(size=8),
plot.caption = element_text(size = 6),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 10),
# Set the entire chart region to blank
panel.background=element_blank(),
plot.background=element_blank(),
#panel.border=element_rect(colour="#F0F0F0"),
# Format the grid
panel.grid.major=element_line(colour="#D0D0D0",size=.2),
axis.ticks=element_blank())
mapTheme <- function(base_size = 12) {
theme(
text = element_text( color = "black"),
plot.title = element_text(size = 16,colour = "black"),
plot.subtitle=element_text(face="italic"),
plot.caption=element_text(hjust=0),
axis.ticks = element_blank(),
panel.background = element_blank(),axis.title = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=2),
strip.text.x = element_text(size = 14))
}
Data Cleaning
There are several steps that we have used to clean our trip data, including both cleaning data fall outside of New York City, and wrong trajectories inside of city.
Clip Trip Data
There are two types of data that are fall outside of New York City. We pick trips in March5th to demonstrate how these data look like. The first type is that there are some data located in Montreal, like the following image shows.
Wrong trajectories falling outisde of NYC-1
Another type of wrong trajectories outside of NYC are trips that are connected to another country, like the following image:
Wrong trajectories falling outisde of NYC-2
In order to clean up these data, we use both clip and spatial intersection. To illustrate, the first type of data could be filtered out by clipping the trip data which are inside NYC boundary. However, for the second type of data, we find out the intersected points of trajectories with the NYC boundary, and then filter out those trips with their unique id. Due to the size of data, we import data which are already cleaned up in the later part, and provide the codes that we used to clean data.
#sample code that we used to clean up data
##Get NYC data
#Reference: http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=927
#NYC <- st_read("E:/NYCDOT/NYS_Civil_Boundaries_SHP/Cities_Towns.shp") %>%
#st_transform(st_crs(March)) %>%
#filter(NAME == "New York")
#NYC_line <- st_cast(NYC, "MULTILINESTRING")
#Marchintersect <- March[NYC_line,]
#March18_cleaned <- March[NYC, ] %>%
#filter(! id %in% Marchintersect$id)There are also trips inside NYC that have strange trajectories. We plan to filter out these data by using several steps.
- Buffering data of bike lanes.
- Transfer geometries of trips data from linestring to points.
- Filter out trip data which overlay with the buffered bike lanes.
- Gorup the points of trip data which are in the same route and have same id, which is identical to each trip.
15 feet buffer
Due to the size of data and hope to keep associated attribute table, we buffer the road data in ArcGIS. During the process of buffering, there is also trade-off:
Buffer Choice
Later, we will use the buffer of bike lanes to find out overlapped trip points. However, as the figure shows, when the buffer becomes wider, there are more overlaps between bike lanes. Sometimes, the trajectories of trips do not fully overlap with the road because the road buffer is not wide enough to cover all the trips. In other words, choosing thinner buffer has potential to loss more data, but choosing thicker buffer brings problems of overlapping roads. In the consideration of this trade-off, we choose to use 15-feet buffer for bike lanes.
Line to Point
Similarly, due to the size of dataset and we would like to keep associated attributes of data, we use Geoprocessing Tool, Generate Pointes Along Lines, in ArcGIS to transfer our geometries from linestring to points. In order to keep the progress consistent, we make the Point Placement by 150 meters. In other words, for one trajectory of bike trip, there will be a point by each 150-meter, including the end points.
Trips on road
Then we use spatial intersect to allocate Street Name on trip points. If the trip points are intersect with buffered lanes, they will be assigned to a Street Name. But there is condition that one trip point will locate on several roads, at the condition when the roads are overlapped with each other at nodes.
Point on Several Roads
Like the point shown in the figure. It is located at the node where LEXINGTON AVENUE LINE, EAST 77 STREET, and LENOX HILL HOSPITAL are overlapped. However, the spatial intersect within st_join function only assign one road name on that point. This process is random but is acceptable because it does not assign multiple Street Name on the point.
Exploratory Analysis
Yearly Trend
We would like to at first have a sense about how the trip data vary over year.
March2018 <- st_read("E:/NYCDOT/rideData_cleaned/March2018/March18_cleaned.shp")
Apr2018 <- st_read("E:/NYCDOT/rideData_cleaned/Apr2018/Apr18_cleaned.shp")
May2018 <- st_read("E:/NYCDOT/rideData_cleaned/May2018/May18_cleaned.shp")
Jun2018 <- st_read("E:/NYCDOT/rideData_cleaned/Jun2018/June18_cleaned.shp")
July2018 <- st_read("E:/NYCDOT/rideData_cleaned/July2018/July18_cleaned.shp")
Aug2018 <- st_read("E:/NYCDOT/rideData_cleaned/Aug2018/Aug18_cleaned.shp")
Sep2018 <- st_read("E:/NYCDOT/rideData_cleaned/Sep2018/Sep18_cleaned.shp")
Oct2018 <- st_read("E:/NYCDOT/rideData_cleaned/Oct2018/Oct18_cleaned.shp")
Nov2018 <- st_read("E:/NYCDOT/rideData_cleaned/Nov2018/Nov18_cleaned.shp")
Dec2018 <- st_read("E:/NYCDOT/rideData_cleaned/Dec2018/Dec18_cleaned.shp")
1.Daily Trend
dayTrend2018 <- rbind(March2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 3),
Apr2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 4),
May2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 5),
Jun2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 6),
July2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 7),
Aug2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 8),
Sep2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 9),
Oct2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 10),
Nov2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 11),
Dec2018 %>%
as.data.frame() %>%
group_by(day) %>%
tally() %>%
mutate(month = 12))ggplot(dayTrend2018, aes(x=day, y=n, color=as.character(month))) +
geom_line() +
labs(title = "Daily Trend in year 2018",
x = "Day",
y = "Count",
colour="Month") +
plotTheme +
theme(plot.title = element_text(size=14),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
About the Daily Trend in 2018, there is no clear and general trend.
2. Hourly Trend
hourTrend2018 <- rbind(March2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 3),
Apr2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 4),
May2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 5),
Jun2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 6),
July2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 7),
Aug2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 8),
Sep2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 9),
Oct2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 10),
Nov2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 11),
Dec2018 %>%
as.data.frame() %>%
group_by(rhour) %>%
tally() %>%
mutate(month = 12))ggplot(hourTrend2018, aes(x=rhour, y=n, color=as.character(month))) +
geom_line() +
labs(title = "Hourly Trend in year 2018",
x = "Hour",
y = "Count",
colour="Month") +
plotTheme +
theme(plot.title = element_text(size=14),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
From the Hourly Trend graph in 2018, the bike trips peak at noon and at late night.
3.Weekday Trend
wdayTrend2018 <- rbind(March2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 3),
Apr2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 4),
May2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 5),
Jun2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 6),
July2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 7),
Aug2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 8),
Sep2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 9),
Oct2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 10),
Nov2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 11),
Dec2018 %>%
as.data.frame() %>%
mutate(wday = wday(time)) %>%
group_by(wday) %>%
tally() %>%
mutate(month = 12))ggplot(wdayTrend2018, aes(x=wday, y=n, color=as.character(month))) +
geom_line() +
scale_x_continuous(breaks = seq(0, 7, by = 1)) +
labs(title = "Weekday Trend in year 2018",
x = "Day of Week",
y = "Count",
colour="Month") +
plotTheme +
theme(plot.title = element_text(size=14),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
If we view the data from the day of week, there is no clear trend about the data. Overall, the number of trips peak at noon and at night, and the later months tend to have more trips than earlier months. There is no clear trend how the data vary among days or days of week.
Borough Level Analysis
Because Manhanttan as our major study hour, we also visualize the density of trips on each roads in Manhanttan, in July 2018 and July 2019.
Due to the size of the dataset, we only choose July 22 pm as our analyzing time.Then, we narrow our data into Manhattan. Because the redundacy of process, the following part just show how we did the process but will not run the code. Later, the processed data will be directly imported.
#import borough Manhanttan
borough <- st_read("E:/NYCDOT/Borough Boundaries/geo_export_4c045526-dcb9-4e1a-b602-59b848e20e6a.shp") %>%
filter(boro_name == "Manhattan") %>%
st_transform(st_crs(July2018))
Manhatan_line <- st_cast(borough, "MULTILINESTRING")
#import bike lane data
bike15 <- st_read("E:/NYCDOT/nyclion_20d/lion_buffer15/lion_buffer15.shp") %>%
st_transform(st_crs(July2018)) %>%
select(Street, FeatureTyp, SegmentTyp, FaceCode, SeqNum, StreetCode, LGC1, SegmentID,
SHAPE_Leng, SHAPE_Area, geometry)
bike15M <- bike15[borough,]##===Import data===##
#import July2018, and July2019 data
#July2018 <- st_read("E:/NYCDOT/rideData_cleaned/July2018/July18_cleaned.shp")
#July2019 <- st_read("E:/NYCDOT/rideData_cleaned/July2019/July19_cleaned.shp")
#Due to the size of dataset, only select rhour==22
#July18_22pm <- July2018 %>%
#filter(rhour == 22)
#July19_22pm <- July2019 %>%
#filter(rhour == 22)
##==select data only in Manhanttan==##
#find out intersection points
#July18_intersect <- July18_22pm[Manhatan_line,]
#July19_intersect <- July19_22pm[Manhatan_line,]
#only in Manhattan
#July18M_22pm <- July18_22pm[borough,] %>%
#filter(!id %in% July18_intersect$id)
#July19M_22pm <- July19_22pm[borough,] %>%
#filter(!id %in% July19_intersect$id)
#check
#mapview(July18M_22pm)
#mapview(July19M_22pm)
##==change trip data's geometry from linestring to points==#
#save the file
#st_write(July18M_22pm, "July18M_22pm.shp", driver = "ESRI Shapefile")
#st_write(July19M_22pm, "July19M_22pm.shp", driver = "ESRI Shapefile")
Visualization
# import results after using ArcGIS (by 150m)
pJuly18M_22pm <- st_read("E:/NYCDOT/riderpoint_shp/July/generated/July18M_22pm_pnts.shp")
pJuly19M_22pm <- st_read("E:/NYCDOT/riderpoint_shp/July/generated/July19M_22pm_pnts.shp")
##==Select points within the roads==##
inrpnts_July18 <- pJuly18M_22pm[bike15M,]
inrpnts_July19 <- pJuly19M_22pm[bike15M,]
#allocate Street Name on roads
inrpnts_July18 <- st_join(inrpnts_July18, bike15M, join = st_intersects, left = TRUE)
inrpnts_July19 <- st_join(inrpnts_July19, bike15M, join = st_intersects, left = TRUE)
##==group points inside same street as linestring==#
#Reference:https://stackoverflow.com/questions/50908771/create-multilines-from-points-grouped-by-id-with-sf-package
pnts2Line_18 <- inrpnts_July18 %>%
group_by(id, Street) %>%
summarise(do_union = FALSE) %>%
st_cast("LINESTRING")
pnts2Line_19 <- inrpnts_July19 %>%
group_by(id, Street) %>%
summarise(do_union = FALSE) %>%
st_cast("LINESTRING")
#Count number of trips on each street
ntrips_July18 <- pnts2Line_18 %>%
st_drop_geometry() %>%
group_by(Street) %>%
summarise(Count = n())
ntrips_July19 <- pnts2Line_19 %>%
st_drop_geometry() %>%
group_by(Street) %>%
summarise(Count = n())
##==bind the trips to road table==##
road_18 <- merge(bike15M %>% select(Street, geometry), ntrips_July18, by = ("Street"), all.x=TRUE)
road_19 <- merge(bike15M %>% select(Street, geometry), ntrips_July19, by = ("Street"), all.x=TRUE)
#add year
road_18$year <- 2018
road_19$year <- 2019##==visualize==##
ggplot() +
geom_sf(data = rbind(road_18, road_19), aes(color = Count), alpha = 0.9)+
facet_wrap(~year) +
scale_colour_viridis(direction = -1,
discrete = FALSE, option="viridis",
na.value="#D4D4D4") +
labs(title = "Bike Trip Counts in July 22pm") +
mapTheme()
Trips Comparsion on Added Bike Lanes
##==Find out the number of trips on new added bike lanes==##
#import data of new bike lanes (buffer 15)
bike18new <- st_read("E:/NYCDOT/bikeDiff/bike_1718Diff/bikelane_1718DiffBuffer.shp") %>%
st_transform(st_crs(July2018))
#clip new bike lanes in Manhanttan
bike18new_M <- bike18new[borough,]
#assign Street Name to new bike lanes
bike18new_M <- st_join(bike18new_M %>% select(-Street), bike15M %>%
select(Street, geometry), join = st_intersects, left = TRUE)
#Find the trip points overlap with roads
tripsinNew18 <- pJuly18M_22pm[bike18new_M,]
tripsinNew19 <- pJuly19M_22pm[bike18new_M,]
#allocate road name on pnts
newtrips18 <- st_join(tripsinNew18, bike18new_M %>% select(Street, geometry),
join = st_intersects, left = TRUE) %>% na.omit()
newtrips19 <- st_join(tripsinNew19, bike18new_M %>% select(Street, geometry),
join = st_intersects, left = TRUE) %>% na.omit()
#group points inside same street as linestring
new.pnts2Line18 <- newtrips18 %>%
group_by(id, Street) %>%
summarise(do_union = FALSE) %>%
st_cast("LINESTRING")
new.pnts2Line19 <- newtrips19 %>%
group_by(id, Street) %>%
summarise(do_union = FALSE) %>%
st_cast("LINESTRING")
#Count
n.new18 <- new.pnts2Line18 %>%
st_drop_geometry() %>%
group_by(Street) %>%
summarise(Count = n()) %>%
mutate(year = 2018)
n.new19 <- new.pnts2Line19 %>%
st_drop_geometry() %>%
group_by(Street) %>%
summarise(Count = n()) %>%
mutate(year = 2019)#Create a summarise table
#Outer join table
new.num <- merge(x = n.new18, y = n.new19, by = "Street", all = TRUE)
#assign NA to 0
new.num[is.na(new.num)] <- 0
#Find out Diff
new.num <- new.num %>%
mutate(Diff = Count.y - Count.x)new.num %>%
select(Street, Diff) %>%
kbl() %>%
kable_styling()| Street | Diff |
|---|---|
| 1 AVENUE | 10 |
| 12 AVENUE | 1 |
| 14 STREET/CANARSIE LINE | 58 |
| 2 AVENUE | 270 |
| 2 AVENUE LINE | 273 |
| 2000 CB BOUNDARY | 33 |
| 2010 CB BOUNDARY | 62 |
| 3 AVE-E 128 STREET PED OVPS | 1 |
| 3 AVENUE | 3 |
| 4 AVENUE | 41 |
| 5 AVENUE | -1 |
| 63 STREET LINE | 18 |
| 7 AVENUE | 237 |
| 7 AVENUE SOUTH | 28 |
| 8 AVENUE | 42 |
| 8 AVENUE LINE | 163 |
| 9 AVENUE | 129 |
| A NEW STREET | 2 |
| ANN STREET | 18 |
| ASTORIA LINE | 9 |
| AVENUE C | 19 |
| AVENUE OF THE AMERICAS | 42 |
| BEAVER STREET | 0 |
| BEDFORD STREET | 17 |
| BEEKMAN STREET | 16 |
| BIKE PATH | 12 |
| BRIDGE STREET | 5 |
| BROAD STREET | 6 |
| BROADWAY LINE | 3 |
| BROOKLYN BRDG PED AND BIKE PATH | 3 |
| BROOKLYN BRIDGE APPROACH | 12 |
| CATHEDRAL PARKWAY | 2 |
| CEDAR STREET | 8 |
| CENTRAL PARK WEST | 18 |
| CENTRE STREET | 12 |
| CHARLTON STREET | 42 |
| CHRISTOPHER STREET | 17 |
| CITY HALL PARK GREENWAY | 3 |
| CLARK STREET LINE | 18 |
| COENTIES ALLEY | 4 |
| COENTIES SLIP | 1 |
| COLUMBUS AVENUE | 24 |
| CT BOUNDARY | 7 |
| DISTRICT BOUNDARY | 191 |
| EAST 110 STREET | 17 |
| EAST 111 STREET | 7 |
| EAST 118 STREET | 3 |
| EAST 119 STREET | 7 |
| EAST 12 STREET | 38 |
| EAST 120 STREET | 2 |
| EAST 126 STREET | 3 |
| EAST 128 STREET | -3 |
| EAST 13 STREET | 21 |
| EAST 23 STREET | 78 |
| EAST 24 STREET | 117 |
| EAST 25 STREET | 44 |
| EAST 25 STREET PEDESTRIAN OVPS | 6 |
| EAST 29 STREET | 58 |
| EAST 3 STREET | 3 |
| EAST 30 STREET | 134 |
| EAST 31 STREET | 86 |
| EAST 32 STREET | 68 |
| EAST 33 STREET | 41 |
| EAST 34 STREET | 3 |
| EAST 35 STREET | 1 |
| EAST 39 STREET | 45 |
| EAST 4 STREET | 4 |
| EAST 40 STREET | 87 |
| EAST 41 STREET | 25 |
| EAST 59 STREET | 12 |
| EAST 60 STREET | 9 |
| EAST 61 STREET | 19 |
| EAST 62 STREET | 18 |
| EAST 63 STREET | 15 |
| EAST 64 STREET | 20 |
| EAST 65 STREET | 16 |
| EAST 66 STREET | 21 |
| EAST 67 STREET | 17 |
| EAST 68 STREET | 9 |
| EAST 7 STREET | 13 |
| EAST 74 STREET | 7 |
| EAST 75 STREET | 14 |
| EAST 76 STREET | 19 |
| EAST 77 STREET | 11 |
| EAST 78 STREET | 4 |
| EAST 78 STREET PEDESTRIAN OVPS | 3 |
| EAST 79 STREET | 13 |
| EAST 8 STREET | 17 |
| EAST 80 STREET | 19 |
| EAST 81 STREET | 13 |
| EAST 82 STREET | 5 |
| EAST 83 STREET | 8 |
| EAST 84 STREET | 5 |
| EAST 89 STREET | 8 |
| EAST 9 STREET | 4 |
| EAST RIVER ESPLANADE | 11 |
| EAST RIVER SHORELINE WEST | 2 |
| EAST RIVER WEST CHANNEL SHL | 3 |
| ED KOCH QUEENSBORO BRIDGE APPR | 12 |
| ED KOCH QUEENSBORO BRIDGE EXIT | 17 |
| EDENS ALLEY | 9 |
| EXCHANGE PLACE | 3 |
| FLETCHER STREET | 2 |
| FRANKLIN D ROOSEVELT DRIVE | 19 |
| FRAWLEY CIRCLE | 6 |
| FREDERICK DOUGLASS BOULEVARD | 19 |
| FRONT STREET | 2 |
| FULTON STREET | 15 |
| GOLD STREET | 25 |
| GOUVERNEUR LANE | 1 |
| GREENWICH AVENUE | 28 |
| GREENWICH STREET | 1 |
| HANOVER SQUARE | 6 |
| HANOVER STREET | 4 |
| HUDSON RIVER GREENWAY | 8 |
| HUDSON RIVER SHORELINE | -2 |
| HUDSON STREET | 10 |
| IRT-1-BROADWAY/ 7 AVENUE LINE | 279 |
| JOE DIMAGGIO HIGHWAY | 1 |
| JOHN FINLEY WALK | 3 |
| JOHN STREET | 10 |
| LENOX AVENUE LINE | -1 |
| LEXINGTON AVENUE | 10 |
| LEXINGTON AVENUE LINE | 63 |
| LIBERTY STREET | -2 |
| LIRR PENN CENTRAL LINE | 68 |
| MAC DOUGAL STREET | 11 |
| MADISON AVENUE | 7 |
| MAIDEN LANE | 1 |
| METRO NORTH-CONNECTING RAIL LINE | 4 |
| MILL LANE | 2 |
| MOORE STREET | 0 |
| NASSAU STREET | 7 |
| NASSAU STREET LINE | 13 |
| OLD SLIP | 6 |
| PARK AVENUE | 13 |
| PARK ROW | 12 |
| PATH-JOURNAL SQ- 33 ST LINE | 17 |
| PEARL STREET | 11 |
| PEDESTRIAN AND BIKE PATH LINK | 0 |
| PEDESTRIAN PATH | 68 |
| PIER | 0 |
| PIER 40 DRIVEWAY | 21 |
| PINE STREET | 7 |
| PLATT STREET | 11 |
| PLEASANT AVENUE | 3 |
| PRINCE STREET | 42 |
| QUEENS MIDTOWN TUNNEL APPROACH | 4 |
| SOUTH STREET | 7 |
| SOUTH WILLIAM STREET | 2 |
| SPRUCE STREET | 22 |
| STATE STREET | 1 |
| STONE STREET | 1 |
| VARICK STREET | 41 |
| WALL STREET | 6 |
| WARREN STREET | 2 |
| WASHINGTON STREET | 20 |
| WATER STREET | 3 |
| WEST 104 STREET | -1 |
| WEST 105 STREET | 1 |
| WEST 106 STREET | 3 |
| WEST 11 STREET | 28 |
| WEST 111 STREET | -1 |
| WEST 114 STREET | 3 |
| WEST 115 STREET | 19 |
| WEST 116 STREET | 17 |
| WEST 12 STREET | 65 |
| WEST 126 STREET | 2 |
| WEST 128 STREET | -3 |
| WEST 13 STREET | 78 |
| WEST 14 STREET | 58 |
| WEST 15 STREET | 47 |
| WEST 16 STREET | 60 |
| WEST 17 STREET | 52 |
| WEST 18 STREET | 58 |
| WEST 19 STREET | 66 |
| WEST 20 STREET | 70 |
| WEST 21 STREET | 75 |
| WEST 22 STREET | 85 |
| WEST 23 STREET | 75 |
| WEST 24 STREET | 39 |
| WEST 25 STREET | 47 |
| WEST 26 STREET | 50 |
| WEST 27 DRIVE | 17 |
| WEST 27 STREET | 49 |
| WEST 28 STREET | 81 |
| WEST 29 STREET | 73 |
| WEST 30 STREET | 29 |
| WEST 42 STREET | 18 |
| WEST 43 STREET | 89 |
| WEST 44 STREET | 24 |
| WEST 59 STREET | 1 |
| WEST 70 STREET | 5 |
| WEST 71 STREET | 10 |
| WEST 72 STREET | 13 |
| WEST 73 STREET | 6 |
| WEST 87 STREET | 14 |
| WEST 88 STREET | 12 |
| WEST 90 STREET | 1 |
| WEST 92 STREET | 4 |
| WEST 93 STREET | 9 |
| WEST 94 STREET | 3 |
| WEST 95 STREET | 5 |
| WEST END AVENUE | 1 |
| WEST HOUSTON STREET | 34 |
| WEST STREET | 34 |
| WHITEHALL STREET | 3 |
| WILLIAM STREET | 13 |
| YORK AVENUE | 8 |
ggplot(data=new.num, aes(x=Street, y=Diff)) +
geom_bar(stat="identity") +
theme(
# Remove grid lines
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# Change axis line
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
) +
labs(title = "Trip Differences of Added Bike Lanes",
subtitle = "Between year 2018 and year 2019 during July 22pm",
y = "Trip Differences")